}
};
+
+// EAGLE3 speculative decoding state
+//
+// Input of draft decoder: (This is different compared to MTP)
+// At "pos P", the decoder takes input pair (t_{P+1}, g_P), with RoPE at P.
+// - t_{P+1} = token at sequence pos P+1 (the *next* token after P)
+// - g_P = encoder output = projection of target's extracted hidden states at P
+//
+// Deferred boundary (MTP doesn't have this issue):
+// Within a single process() call with n_tokens, we can only write decoder KV for
+// training pos 0..n_tokens-2. The last training pos (n_tokens-1) needs t_{n_tokens}
+// which lies *outside* this batch — it is the token target will sample next or the first token from next ubatch.
+// So the last training pos of each process() call is *deferred* to whichever next call has
+// the missing token in hand:
+// - multi-ubatch prefill: the next process()'s first token completes the pair
+// (handled by the per-seq "cross-ubatch bridge")
+// - single-ubatch prefill / after verify: draft()'s seed step uses "dp.id_last"
+// (target's freshest sample) to complete the pair
+//
+// Per-seq carry-over state:
+// pending_g_last [n_embd_dec] ┐ the deferred boundary's (g, pos). Set by
+// pending_pos_last llama_pos ┘ process() at end of ubatch (= last row);
+// rebased by accept() to first-non-accepted pos.
+// verify_g [N × n_embd_dec] snapshot of process()'s encoder output;
+// verify_pos_first llama_pos consumed by accept() to recover the right
+// verify_g_rows int32_t pending_g_last row for any n_accepted value.
+//
+// Performance is overall good but there is waste in verify cycle:
+// process() runs encoder + decoder on the *full* verify batch including rows for
+// rejected drafts. The KV at those positions is then dropped.
+//
+// TODO: Not sure if we need optimization for this waste?
+// If so we may need hybrid stash:
+// in verify mode, have process() only stash features and let draft() seed run
+// encoder+decoder on n_accepted+1 rows).
struct common_speculative_impl_draft_eagle3 : public common_speculative_impl {
- //common_params_speculative_eagle3 params;
+ common_params_speculative_draft params;
+ llama_batch batch;
+
+ std::vector<common_sampler_ptr> smpls;
+
+ int32_t n_embd_dec = 0; // draft hidden size
+ int32_t n_embd_enc = 0; // target_layer_ids_n * target_hidden_size
+ int32_t n_embd_tgt = 0; // target model hidden size
+
+ const int32_t * target_layer_ids = nullptr; // model_dft's extract layer indices
+ uint32_t target_layer_ids_n = 0;
+
+ // [per-seq] deferred boundary state
+ std::vector<std::vector<float>> pending_g_last;
+ std::vector<llama_pos> pending_pos_last;
+
+ // [per-seq] snapshot of the most recent process()'s encoder output
+ std::vector<std::vector<float>> verify_g; // [n_seq][n_rows * n_embd_dec]
+ std::vector<llama_pos> verify_pos_first; // [n_seq] — pos of verify_g[seq][0]
+ std::vector<int32_t> verify_g_rows; // [n_seq] — number of rows
+
+ // scratch buffer for concatenated target features [n_tokens, n_embd_enc]
+ std::vector<float> features_buf;
+ std::vector<float> g_embd_buf;
common_speculative_impl_draft_eagle3(const common_params_speculative & params, uint32_t n_seq)
: common_speculative_impl(COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3, n_seq)
+ , params(params.draft)
{
LOG_INF("%s: adding speculative implementation 'draft-eagle3'\n", __func__);
LOG_INF("%s: - n_max=%d, n_min=%d, p_min=%f\n", __func__, params.draft.n_max, params.draft.n_min, params.draft.p_min);
+
+ auto * ctx_tgt = this->params.ctx_tgt;
+ auto * ctx_dft = this->params.ctx_dft;
+ GGML_ASSERT(ctx_tgt && ctx_dft && "EAGLE3 requires ctx_tgt and ctx_dft to be set");
+
+ const llama_model * model_dft = llama_get_model(ctx_dft);
+ const llama_model * model_tgt = llama_get_model(ctx_tgt);
+
+ target_layer_ids = llama_model_target_layer_ids (model_dft);
+ target_layer_ids_n = llama_model_target_layer_ids_n(model_dft);
+ if (target_layer_ids_n != 3) {
+ throw std::runtime_error("draft model is not eagle3 (expected 3 extract layers, got " +
+ std::to_string(target_layer_ids_n) + ")");
+ }
+
+ n_embd_tgt = llama_model_n_embd(model_tgt);
+ n_embd_dec = llama_model_n_embd(model_dft);
+ n_embd_enc = (int32_t) target_layer_ids_n * n_embd_tgt;
+
+ const int32_t n_b = (int32_t) llama_n_batch(ctx_dft);
+ batch = llama_batch_init(/*n_tokens=*/ n_b, /*embd=*/ n_embd_dec, /*n_seq_max=*/ 1);
+ // llama_batch_init allocates only one of token/embd; eagle3 decoder needs both.
+ // TODO: fix, how to call without malloc
+ batch.token = (llama_token *) malloc(sizeof(llama_token) * n_b);
+
+ smpls.resize(n_seq);
+ for (auto & s : smpls) {
+ common_params_sampling sparams;
+ sparams.no_perf = false;
+ sparams.top_k = 10;
+ sparams.samplers = { COMMON_SAMPLER_TYPE_TOP_K };
+ s.reset(common_sampler_init(llama_get_model(ctx_dft), sparams));
+ }
+
+ // turn on extraction of the target layers' input embeddings
+ for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+ llama_set_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k], true);
+ }
+
+ // turn on extraction of the draft model's pre-norm hidden state
+ // (used both for the encoder output g_embd and the decoder pre-norm output).
+ llama_set_embeddings_nextn(ctx_dft, true, /*masked*/ true);
+
+ pending_g_last.assign(n_seq, std::vector<float>(n_embd_dec, 0.0f));
+ pending_pos_last.assign(n_seq, -1);
+
+ verify_g.assign(n_seq, std::vector<float>());
+ verify_pos_first.assign(n_seq, -1);
+ verify_g_rows.assign(n_seq, 0);
}
- void begin(llama_seq_id /*seq_id*/, const llama_tokens & /*prompt*/) override {
- // noop
+ ~common_speculative_impl_draft_eagle3() override {
+ if (batch.token != nullptr) {
+ free(batch.token);
+ batch.token = nullptr;
+ }
+ llama_batch_free(batch);
}
- bool process(const llama_batch & /*batch*/) override {
- // TODO: implement
+ void begin(llama_seq_id seq_id, const llama_tokens & prompt) override {
+ const int32_t N = (int32_t) prompt.size();
+ if (N <= 0) {
+ return;
+ }
+ // expected state after prefill: ctx_dft has pos 0..N-2 (last position is deferred to
+ // draft()'s seed step). Warn only if more than one position is missing.
+ auto * ctx_dft = this->params.ctx_dft;
+ const llama_pos pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+ if (pos_max < N - 2) {
+ LOG_WRN("%s: ctx_dft pos_max=%d < N-2=%d — process() did not run on every prefill ubatch. "
+ "Drafts may degrade.\n",
+ __func__, (int) pos_max, N - 2);
+ }
+ }
+
+ bool process(const llama_batch & batch_in) override {
+ if (batch_in.n_tokens <= 0) {
+ return true;
+ }
+
+ if (batch_in.token == nullptr || batch_in.embd != nullptr) {
+ return true;
+ }
+
+ const int32_t n_tokens = batch_in.n_tokens;
+
+ // i_batch_beg[seq] / i_batch_end[seq]: inclusive batch indices of this seq's
+ // first/last token in batch_in. Assumes per-seq tokens are contiguous within
+ // the ubatch (server's default ordering).
+ std::vector<int32_t> i_batch_beg(n_seq, -1);
+ std::vector<int32_t> i_batch_end(n_seq, -1);
+ for (int k = 0; k < n_tokens; ++k) {
+ GGML_ASSERT(batch_in.n_seq_id[k] == 1);
+ const llama_seq_id seq_id = batch_in.seq_id[k][0];
+ if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+ continue;
+ }
+ i_batch_end[seq_id] = k;
+ if (i_batch_beg[seq_id] < 0) {
+ i_batch_beg[seq_id] = k;
+ }
+ }
+
+ auto * ctx_tgt = this->params.ctx_tgt;
+ auto * ctx_dft = this->params.ctx_dft;
+
+ // Interleave each extract_layer's hidden state into a contiguous buffer of
+ // shape [n_tokens, target_layer_ids_n * n_embd_tgt]. Then run EAGLE3 encoder
+ // to get one g_embd row per token.
+ features_buf.resize((size_t) n_tokens * n_embd_enc, 0.0f);
+
+ for (uint32_t k = 0; k < target_layer_ids_n; ++k) {
+ const float * layer = llama_get_embeddings_layer_inp(ctx_tgt, (uint32_t) target_layer_ids[k]);
+ if (!layer) {
+ GGML_ABORT("EAGLE3: target layer %d input not extracted.", target_layer_ids[k]);
+ }
+ for (int32_t i = 0; i < n_tokens; ++i) {
+ float * dst = features_buf.data() + (size_t) i * n_embd_enc + k * (size_t) n_embd_tgt;
+ const float * src = layer + (size_t) i * n_embd_tgt;
+ std::memcpy(dst, src, (size_t) n_embd_tgt * sizeof(float));
+ }
+ }
+
+ g_embd_buf.resize((size_t) n_tokens * n_embd_dec);
+
+ // llama_encode() requires the full encoder batch to fit in n_ubatch.
+ // Allow batch > ubatch: eagle3's per-token encoder can be chunked safely.
+ const int32_t n_ubatch_dft = (int32_t) llama_n_ubatch(ctx_dft);
+ for (int32_t i = 0; i < n_tokens; i += n_ubatch_dft) {
+ const int32_t n_chunk = std::min(n_ubatch_dft, n_tokens - i);
+
+ llama_batch enc_batch = {
+ /*.n_tokens =*/ n_chunk,
+ /*.token =*/ nullptr,
+ /*.embd =*/ features_buf.data() + (size_t) i * n_embd_enc,
+ /*.pos =*/ nullptr,
+ /*.n_seq_id =*/ nullptr,
+ /*.seq_id =*/ nullptr,
+ /*.logits =*/ nullptr,
+ };
+ const int32_t rc = llama_encode(ctx_dft, enc_batch);
+ if (rc != 0) {
+ LOG_ERR("%s: llama_encode(ctx_dft) failed rc=%d (n_tokens=%d, offset=%d)\n",
+ __func__, rc, (int) n_chunk, (int) i);
+ return false;
+ }
+
+ // g_embd has shape [n_chunk, n_embd_dec] in ctx_dft's pre-norm embeddings buffer.
+ const float * g_embd_chunk = llama_get_embeddings_nextn(ctx_dft);
+ GGML_ASSERT(g_embd_chunk && "EAGLE3 encoder produced no output.");
+ std::memcpy(g_embd_buf.data() + (size_t) i * n_embd_dec,
+ g_embd_chunk,
+ (size_t) n_chunk * n_embd_dec * sizeof(float));
+ }
+
+ const float * g_embd = g_embd_buf.data();
+
+ const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+ // EAGLE3 decoder input convention: at memory pos P the input pair is
+ // (token[P+1], g_embd[P]). This shifts the token index "left by one" relative to g_embd.
+ //
+ // Per seq, in order:
+ // (a) cross-ubatch bridge — when applicable, write the previously-deferred
+ // pos using this ubatch's first token + pending_g_last.
+ // (b) main write loop — for k in [beg, end-1], write (token[k+1], g_embd[k])
+ // at pos[k]. The last training pos (k=end) is left unwritten = new
+ // deferred boundary, completed by the next process() or draft() call.
+ // (c) refresh deferred state — stash this ubatch's full g_embd into verify_g,
+ // update pending_g_last / pending_pos_last to the last row.
+ common_batch_clear(batch);
+
+ for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+ const int32_t beg = i_batch_beg[seq_id];
+ const int32_t end = i_batch_end[seq_id];
+ if (beg < 0 || end < 0) {
+ continue;
+ }
+
+ // cross-ubatch bridge — complete the prior ubatch's deferred boundary.
+ // Fires iff all three preconditions hold:
+ // 1) pending_pos_last >= 0
+ // 2) pending_pos_last + 1 == pos[beg]
+ // 3) pending_pos_last > dft_pos_max // TODO: is this check needed?
+ const llama_pos pending_pos = pending_pos_last[seq_id];
+ if (pending_pos >= 0 && pending_pos + 1 == batch_in.pos[beg]) {
+ const llama_pos dft_pos_max = llama_memory_seq_pos_max(llama_get_memory(ctx_dft), seq_id);
+ if (pending_pos > dft_pos_max) {
+ common_batch_add(batch, batch_in.token[beg], pending_pos, { seq_id }, /*logits=*/ false);
+ std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+ pending_g_last[seq_id].data(), row_bytes);
+ }
+ }
+
+ for (int32_t k = beg; k < end; ++k) {
+ common_batch_add(batch, batch_in.token[k + 1], batch_in.pos[k], { seq_id }, /*logits=*/ false);
+ std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+ g_embd + (size_t) k * n_embd_dec, row_bytes);
+ }
+
+ // refresh deferred state
+ const int32_t n_rows = end - beg + 1;
+ verify_pos_first[seq_id] = batch_in.pos[beg];
+ pending_pos_last[seq_id] = batch_in.pos[end];
+ verify_g_rows[seq_id] = n_rows;
+ verify_g[seq_id].resize((size_t) n_rows * n_embd_dec, 0.0f);
+ std::memcpy(verify_g[seq_id].data(), g_embd + (size_t) beg * n_embd_dec, row_bytes * n_rows);
+ std::memcpy(pending_g_last[seq_id].data(), g_embd + (size_t) end * n_embd_dec, row_bytes);
+ }
+
+ if (batch.n_tokens > 0) {
+ const int32_t rc = llama_decode(ctx_dft, batch);
+ if (rc != 0) {
+ LOG_ERR("%s: llama_decode(ctx_dft) failed rc=%d (n_tokens=%d, ubatch_pos[0]=%d)\n",
+ __func__, rc, (int) batch.n_tokens, (int) batch_in.pos[0]);
+ return false;
+ }
+ }
+
return true;
}
- void draft(common_speculative_draft_params_vec & /*dparams*/) override {
- // TODO: implement
+ void draft(common_speculative_draft_params_vec & dparams) override {
+ auto & ctx_dft = params.ctx_dft;
+
+ common_batch_clear(batch);
+
+ // keep track of which sequences are still drafting
+ int n_drafting = 0;
+ std::vector<bool> drafting(n_seq);
+
+ const size_t row_bytes = (size_t) n_embd_dec * sizeof(float);
+
+ // Complete the deferred boundary pair (dp.id_last, pending_g_last) at memory
+ // pos pending_pos_last. dp.id_last is target's freshest sample (= corrected
+ // token after verify, or first generated token after prefill), matching the
+ // EAGLE3 input convention (token[P+1], g_embd[P]) at pos P.
+ for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+ auto & dp = dparams[seq_id];
+
+ if (!dp.drafting) {
+ continue;
+ }
+ if (pending_pos_last[seq_id] < 0) {
+ continue;
+ }
+
+ n_drafting++;
+ drafting[seq_id] = true;
+ common_sampler_reset(smpls[seq_id].get());
+
+ llama_memory_seq_rm(llama_get_memory(ctx_dft), seq_id, pending_pos_last[seq_id], -1);
+
+ common_batch_add(batch, dp.id_last, pending_pos_last[seq_id], { seq_id }, true);
+ std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec,
+ pending_g_last[seq_id].data(),
+ row_bytes);
+ }
+
+ if (batch.n_tokens == 0) {
+ return;
+ }
+
+ int ret = llama_decode(ctx_dft, batch);
+ if (ret != 0) {
+ LOG_WRN("%s: llama_decode returned %d\n", __func__, ret);
+ return;
+ }
+
+ int i = 0;
+
+ while (n_drafting > 0) {
+ int i_batch = 0;
+
+ common_batch_clear(batch);
+
+ for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+ if (!drafting[seq_id]) {
+ continue;
+ }
+
+ auto * smpl = smpls[seq_id].get();
+
+ common_sampler_sample(smpl, ctx_dft, i_batch, true);
+ // pre-norm hidden state of this position becomes g_embd for the next step
+ const float * prenorm = llama_get_embeddings_nextn_ith(ctx_dft, i_batch);
+ ++i_batch;
+
+ const auto * cur_p = common_sampler_get_candidates(smpl, true);
+
+ for (int k = 0; k < std::min(3, (int) cur_p->size); ++k) {
+ LOG_DBG(" - seq_id %d, draft candidate %3d, pos %3d: %6d (%8.3f) '%s'\n",
+ seq_id, k, i, cur_p->data[k].id, cur_p->data[k].p,
+ common_token_to_piece(ctx_dft, cur_p->data[k].id).c_str());
+ }
+
+ const llama_token id = cur_p->data[0].id;
+
+ // only collect very high-confidence draft tokens
+ // (configurable via --spec-draft-p-min, set to 0.0 to disable early-stop)
+ if (cur_p->data[0].p < params.p_min) {
+ drafting[seq_id] = false;
+ n_drafting--;
+
+ continue;
+ }
+
+ common_sampler_accept(smpl, id, true);
+
+ auto & dp = dparams.at(seq_id);
+ auto & result = *dp.result;
+
+ result.push_back(id);
+
+ if (params.n_max <= (int) result.size()) {
+ drafting[seq_id] = false;
+ n_drafting--;
+ continue;
+ }
+
+ common_batch_add(batch, id, pending_pos_last[seq_id] + (i + 1), { seq_id }, true);
+ std::memcpy(batch.embd + (size_t) (batch.n_tokens - 1) * n_embd_dec, prenorm, row_bytes);
+ }
+
+ if (batch.n_tokens == 0) {
+ break;
+ }
+
+ ret = llama_decode(ctx_dft, batch);
+ if (ret != 0) {
+ LOG_WRN("%s: llama_decode[%d] returned %d\n", __func__, i, ret);
+ break;
+ }
+
+ ++i;
+ }
+
+ for (llama_seq_id seq_id = 0; seq_id < (llama_seq_id) n_seq; ++seq_id) {
+ auto & dp = dparams[seq_id];
+ if (!dp.drafting) {
+ continue;
+ }
+
+ if (dp.result->size() < (size_t) params.n_min) {
+ dp.result->clear();
+ }
+ }
}
- void accept(llama_seq_id /*seq_id*/, uint16_t /*n_accepted*/, bool /*is_other*/) override {
- // noop
+ void accept(llama_seq_id seq_id, uint16_t n_accepted, bool /*is_other*/) override {
+ if (seq_id < 0 || seq_id >= (llama_seq_id) n_seq) {
+ return;
+ }
+
+ const int32_t n_rows = verify_g_rows[seq_id];
+ if (n_rows <= 0) {
+ return;
+ }
+
+ const int32_t i_g = std::min<int32_t>(n_accepted, n_rows - 1);
+ pending_pos_last[seq_id] = verify_pos_first[seq_id] + i_g;
+ std::memcpy(pending_g_last[seq_id].data(),
+ verify_g[seq_id].data() + (size_t) i_g * n_embd_dec,
+ (size_t) n_embd_dec * sizeof(float));
}
bool need_embd() const override {
uint32_t enabled_configs = common_get_enabled_speculative_configs(params.types);
bool has_draft_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_SIMPLE));
- bool has_draft_eagle3 = false; // TODO PR-18039: if params.speculative.eagle3
+ bool has_draft_eagle3 = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_EAGLE3)) && params.draft.ctx_dft != nullptr;
bool has_mtp = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_DRAFT_MTP)) && params.draft.ctx_dft != nullptr;
+
+
bool has_ngram_cache = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_CACHE));
bool has_ngram_simple = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_SIMPLE));
bool has_ngram_map_k = (enabled_configs & (1u << COMMON_SPECULATIVE_TYPE_NGRAM_MAP_K));
"LlamaBidirectionalModel": "llama",
"LlamaForCausalLM": "llama",
"LlamaModel": "llama",
+ "Eagle3DraftModel": "llama",
+ "Eagle3Speculator": "llama",
+ "LlamaForCausalLMEagle3": "llama",
"LlavaForConditionalGeneration": "llama",
"LlavaStableLMEpochForCausalLM": "stablelm",
"MPTForCausalLM": "mpt",
metadata: gguf.Metadata
dir_model_card: Path
remote_hf_model_id: str | None
+ target_model_dir: Path | None
# subclasses should define this!
model_arch: gguf.MODEL_ARCH
small_first_shard: bool = False, hparams: dict[str, Any] | None = None, remote_hf_model_id: str | None = None,
disable_mistral_community_chat_template: bool = False,
sentence_transformers_dense_modules: bool = False,
+ target_model_dir: Path | None = None,
fuse_gate_up_exps: bool = False,
fp8_as_q8: bool = False):
if type(self) is ModelBase or \
self.dry_run = dry_run
self.remote_hf_model_id = remote_hf_model_id
self.sentence_transformers_dense_modules = sentence_transformers_dense_modules
+ self.target_model_dir = target_model_dir
self.fuse_gate_up_exps = fuse_gate_up_exps
self._gate_exp_buffer: dict[int, Tensor] = {}
self._up_exp_buffer: dict[int, Tensor] = {}
torch.float16: np.float16,
torch.float32: np.float32,
torch.uint8: np.uint8,
+ torch.int64: np.int64,
}
# only used when byteswapping data. Only correct size is needed
from typing import Callable, Iterable, TYPE_CHECKING
+import numpy as np
import torch
if TYPE_CHECKING:
from torch import Tensor
-from .base import ModelBase, TextModel, gguf
+from .base import ModelBase, TextModel, gguf, logger
@ModelBase.register(
"VLlama3ForCausalLM",
"LlavaForConditionalGeneration",
"VoxtralForConditionalGeneration",
+ "LlamaForCausalLMEagle3",
+ "Eagle3Speculator",
+ "Eagle3DraftModel",
"IQuestCoderForCausalLM",
"LlamaModel")
class LlamaModel(TextModel):
hparams = ModelBase.load_hparams(self.dir_model, is_mistral_format=False)
self.origin_hf_arch = hparams.get('architectures', [None])[0]
+ # Detect eagle3 draft checkpoint by hparams (some models don't use a distinct HF arch name)
+ if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+ self.is_eagle3 = True
+ self.model_arch = gguf.MODEL_ARCH.EAGLE3
+ logger.info("Detected EAGLE-3 draft model, switching to EAGLE3 architecture")
+ # Re-initialize tensor_map with eagle3 architecture
+ self.tensor_map = gguf.get_tensor_name_map(self.model_arch, self.block_count)
+ # Update gguf_writer architecture
+ self.gguf_writer.arch = gguf.MODEL_ARCH_NAMES[self.model_arch]
+ self.gguf_writer.add_architecture()
+ if self.target_model_dir is None:
+ raise ValueError(
+ "EAGLE-3 model requires --target-model-dir to be specified. "
+ "Please provide the path to the target model directory to read config.json"
+ )
+ # Read both eagle3 raw config and target model config
+ with open(self.dir_model / "config.json", 'r', encoding='utf-8') as f:
+ eagle3_raw_config = json.load(f)
+ with open(self.target_model_dir / "config.json", 'r', encoding='utf-8') as f:
+ target_config = json.load(f)
+
+ if "text_config" in target_config:
+ target_config = {**target_config, **target_config["text_config"]}
+ self.target_vocab_size = target_config["vocab_size"]
+
+ # target_layers: derived from target model layer count (low/mid/high)
+ target_num_layers = target_config["num_hidden_layers"]
+ target_layers = [2, target_num_layers // 2, target_num_layers - 3]
+ logger.info(f"EAGLE-3: target_layers = {target_layers} (target model has {target_num_layers} layers)")
+ self.gguf_writer.add_array(f"{self.gguf_writer.arch}.target_layers", target_layers)
+
+ # target_hidden_size: prefer eagle3 config, fallback to target config
+ if eagle3_raw_config.get("target_hidden_size") is not None:
+ target_hidden_size = eagle3_raw_config["target_hidden_size"]
+ src = "EAGLE-3 config"
+ else:
+ target_hidden_size = target_config["hidden_size"]
+ src = "target model config"
+ logger.info(f"EAGLE-3: target_hidden_size = {target_hidden_size} (from {src})")
+ self.gguf_writer.add_uint32(f"{self.gguf_writer.arch}.target_hidden_size", target_hidden_size)
+
+ # norm_before_residual (RedHat-style eagle3 specific)
+ norm_before_residual = eagle3_raw_config.get("norm_before_residual", False)
+ logger.info(f"EAGLE-3: norm_before_residual = {norm_before_residual}")
+ self.gguf_writer.add_bool(f"{self.gguf_writer.arch}.norm_before_residual", norm_before_residual)
+
def set_vocab(self):
+ # eagle3: use tokenizer from target model if provided
+ original_dir_model = None
+ if getattr(self, 'is_eagle3', False):
+ assert self.target_model_dir is not None
+ logger.info(f"EAGLE-3: Using tokenizer from target model: {self.target_model_dir}")
+ original_dir_model = self.dir_model
+ self.dir_model = self.target_model_dir
+
if self.origin_hf_arch == "GlmasrModel":
return self._set_vocab_glmedge()
if self.hparams.get("vocab_size", 32000) == 49152:
self.gguf_writer.add_add_bos_token(False)
+ # eagle3: Restore original dir_model
+ if original_dir_model is not None:
+ self.dir_model = original_dir_model
+
def set_gguf_parameters(self):
super().set_gguf_parameters()
hparams = self.hparams
return super().filter_tensors((name, gen))
+ def index_tensors(self, remote_hf_model_id: str | None = None) -> dict[str, Callable[[], Tensor]]:
+ tensors = super().index_tensors(remote_hf_model_id)
+
+ # Handle Eagle3Speculator nested config
+ if "transformer_layer_config" in self.hparams:
+ self.hparams = {**self.hparams, **self.hparams["transformer_layer_config"]}
+
+ # eagle3 detection
+ if "draft_vocab_size" in self.hparams and self.hparams["num_hidden_layers"] == 1:
+ logger.info("EAGLE-3: renaming midlayer.* / layers.0.* to model.layers.0.*")
+ new_tensors = {}
+ for name, gen in tensors.items():
+ if name.startswith("midlayer."):
+ new_name = "model.layers.0." + name[len("midlayer."):]
+ new_tensors[new_name] = gen
+ elif name.startswith("layers.0."): # Eagle3Speculator format
+ new_name = "model." + name
+ new_tensors[new_name] = gen
+ else:
+ new_tensors[name] = gen
+ return new_tensors
+
+ return tensors
+
def modify_tensors(self, data_torch: Tensor, name: str, bid: int | None) -> Iterable[tuple[str, Tensor]]:
+ # eagle3: special tensors that bypass standard llama mapping
+ if getattr(self, 'is_eagle3', False):
+ if name == "fc.weight":
+ yield (name, data_torch)
+ return
+ if name == "d2t":
+ # store for manual int64 handling in prepare_tensors (avoid F32 conversion)
+ if not hasattr(self, '_eagle3_int_tensors'):
+ self._eagle3_int_tensors = {}
+ self._eagle3_int_tensors[name] = data_torch
+ return
+ if name == "t2d":
+ # not used at runtime, skip
+ return
+ if name.endswith(".hidden_norm.weight"):
+ yield (self.format_tensor_name(gguf.MODEL_TENSOR.ATTN_NORM_2, bid), data_torch)
+ return
+
n_head = self.find_hparam(["n_heads", "num_attention_heads"])
n_kv_head = self.find_hparam(["n_kv_heads", "num_key_value_heads"])
yield (self.format_tensor_name(gguf.MODEL_TENSOR.ROPE_FREQS), torch.tensor(rope_factors, dtype=torch.float32))
def prepare_tensors(self):
+ # eagle3: collect d2t original dtype before parent converts tensors to F32
+ eagle3_original_dtypes = {}
+ if getattr(self, 'is_eagle3', False):
+ for name, data_torch in self.get_tensors():
+ if name == "d2t":
+ eagle3_original_dtypes[name] = data_torch.dtype
+
super().prepare_tensors()
+ # eagle3: write d2t as absolute target token ids
+ if getattr(self, 'is_eagle3', False) and hasattr(self, '_eagle3_int_tensors'):
+ for name, data_torch in self._eagle3_int_tensors.items():
+ old_dtype = eagle3_original_dtypes.get(name, data_torch.dtype)
+ data = data_torch.to(torch.int64).cpu().numpy()
+ if name == "d2t":
+ data = data.reshape(-1)
+ data = data + np.arange(data.size, dtype=np.int64)
+ if np.any((data < 0) | (data >= self.target_vocab_size)):
+ raise ValueError(f"EAGLE-3 d2t target ids out of range for target vocab size {self.target_vocab_size}")
+ if np.unique(data).size != data.size:
+ raise ValueError("EAGLE-3 d2t contains duplicate target ids")
+ data_qtype = gguf.GGMLQuantizationType.I64
+
+ shape_str = f"{{{', '.join(str(n) for n in reversed(data.shape))}}}"
+ logger.info(f"{name + ',':<30} {old_dtype} --> {data_qtype.name}, shape = {shape_str}")
+ self.gguf_writer.add_tensor(name, data, raw_dtype=data_qtype)
+
if self._experts is not None:
# flatten `list[dict[str, Tensor]]` into `list[str]`
experts = [k for d in self._experts for k in d.keys()]
help="Store tensors dequantized from FP8 as Q8_0 instead of BF16/F16.",
)
+ parser.add_argument(
+ "--target-model-dir", type=str, default=None,
+ help=(
+ "path to the target model directory; required when converting a standalone draft model "
+ "(e.g. EAGLE3 / DFlash) that needs target-model metadata such as tokenizer, hidden size, and "
+ "layer count to populate its GGUF."
+ ),
+ )
+
args = parser.parse_args()
if not args.print_supported_models and args.model is None:
parser.error("the following arguments are required: model")
small_first_shard=args.no_tensor_first_split,
remote_hf_model_id=hf_repo_id, disable_mistral_community_chat_template=disable_mistral_community_chat_template,
sentence_transformers_dense_modules=args.sentence_transformers_dense_modules,
+ target_model_dir=Path(args.target_model_dir) if args.target_model_dir else None,
fuse_gate_up_exps=args.fuse_gate_up_exps,
fp8_as_q8=args.fp8_as_q8,
)
HIDDEN_ACT = "{arch}.hidden_activation"
DENSE_FEAT_IN_SIZE = "{arch}.{dense}_feat_in"
DENSE_FEAT_OUT_SIZE = "{arch}.{dense}_feat_out"
+ TARGET_LAYERS = "{arch}.target_layers"
+ TARGET_HIDDEN_SIZE = "{arch}.target_hidden_size"
+ NORM_BEFORE_RESIDUAL = "{arch}.norm_before_residual"
class Attention:
HEAD_COUNT = "{arch}.attention.head_count"
RND1 = auto()
PANGU_EMBED = auto()
MISTRAL3 = auto()
+ EAGLE3 = auto()
MISTRAL4 = auto()
PADDLEOCR = auto()
MIMO2 = auto()
A_PER_DIM_K_SCALE = auto() # gemma4
A_PER_DIM_SCALE = auto() # gemma4
# nextn/mtp
- NEXTN_PROJ_PRE = auto()
- NEXTN_PROJ_POST = auto()
- NEXTN_EH_PROJ = auto()
- NEXTN_EMBED_TOKENS = auto()
- NEXTN_ENORM = auto()
- NEXTN_HNORM = auto()
+ NEXTN_PROJ_PRE = auto()
+ NEXTN_PROJ_POST = auto()
+ NEXTN_EH_PROJ = auto()
+ NEXTN_EMBED_TOKENS = auto()
+ NEXTN_ENORM = auto()
+ NEXTN_HNORM = auto()
NEXTN_SHARED_HEAD_HEAD = auto()
NEXTN_SHARED_HEAD_NORM = auto()
+ # eagle3
+ FC = auto() # feature fusion layer
+ D2T = auto() # draft to target vocabulary mapping
# lfm2 audio
A_ENC_NORM_CONV = auto()
A_ENC_LINEAR_POS = auto()
MODEL_ARCH.RND1: "rnd1",
MODEL_ARCH.PANGU_EMBED: "pangu-embedded",
MODEL_ARCH.MISTRAL3: "mistral3",
+ MODEL_ARCH.EAGLE3: "eagle3",
MODEL_ARCH.MISTRAL4: "mistral4",
MODEL_ARCH.PADDLEOCR: "paddleocr",
MODEL_ARCH.MIMO2: "mimo2",
MODEL_TENSOR.POS_EMBD: "position_embd",
MODEL_TENSOR.OUTPUT_NORM: "output_norm",
MODEL_TENSOR.OUTPUT: "output",
- MODEL_TENSOR.DENSE_2_OUT: "dense_2", # embeddinggemma 2_Dense
- MODEL_TENSOR.DENSE_3_OUT: "dense_3", # embeddinggemma 2_Dense
+ MODEL_TENSOR.DENSE_2_OUT: "dense_2", # embeddinggemma 2_Dense
+ MODEL_TENSOR.DENSE_3_OUT: "dense_3", # embeddinggemma 2_Dense
MODEL_TENSOR.ROPE_FREQS: "rope_freqs",
MODEL_TENSOR.ROPE_FACTORS_LONG: "rope_factors_long",
MODEL_TENSOR.ROPE_FACTORS_SHORT: "rope_factors_short",
MODEL_TENSOR.NEXTN_HNORM: "blk.{bid}.nextn.hnorm",
MODEL_TENSOR.NEXTN_SHARED_HEAD_HEAD: "blk.{bid}.nextn.shared_head_head",
MODEL_TENSOR.NEXTN_SHARED_HEAD_NORM: "blk.{bid}.nextn.shared_head_norm",
+ MODEL_TENSOR.FC: "fc",
+ MODEL_TENSOR.D2T: "d2t",
}
MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
MODEL_TENSOR.FFN_DOWN_EXP,
MODEL_TENSOR.FFN_UP_EXP,
],
+ MODEL_ARCH.EAGLE3: [
+ MODEL_TENSOR.TOKEN_EMBD,
+ MODEL_TENSOR.OUTPUT_NORM,
+ MODEL_TENSOR.OUTPUT,
+ MODEL_TENSOR.ROPE_FREQS,
+ MODEL_TENSOR.ATTN_NORM,
+ MODEL_TENSOR.ATTN_NORM_2,
+ MODEL_TENSOR.ATTN_Q,
+ MODEL_TENSOR.ATTN_K,
+ MODEL_TENSOR.ATTN_V,
+ MODEL_TENSOR.ATTN_OUT,
+ MODEL_TENSOR.FFN_NORM,
+ MODEL_TENSOR.FFN_GATE,
+ MODEL_TENSOR.FFN_DOWN,
+ MODEL_TENSOR.FFN_UP,
+ MODEL_TENSOR.FC,
+ MODEL_TENSOR.D2T,
+ ],
MODEL_ARCH.MISTRAL4: [
MODEL_TENSOR.TOKEN_EMBD,
MODEL_TENSOR.OUTPUT_NORM,
#include "llama-impl.h"
#include <map>
-#include <set>
#include <vector>
static const std::map<llm_arch, const char *> LLM_ARCH_NAMES = {
{ LLM_ARCH_RND1, "rnd1" },
{ LLM_ARCH_PANGU_EMBED, "pangu-embedded" },
{ LLM_ARCH_MISTRAL3, "mistral3" },
+ { LLM_ARCH_EAGLE3, "eagle3" },
{ LLM_ARCH_MISTRAL4, "mistral4" },
{ LLM_ARCH_PADDLEOCR, "paddleocr" },
{ LLM_ARCH_MIMO2, "mimo2" },
{ LLM_KV_CLASSIFIER_OUTPUT_LABELS, "%s.classifier.output_labels" },
+ { LLM_KV_TARGET_LAYERS, "%s.target_layers" },
+ { LLM_KV_TARGET_HIDDEN_SIZE, "%s.target_hidden_size" },
+ { LLM_KV_NORM_BEFORE_RESIDUAL, "%s.norm_before_residual" },
+
{ LLM_KV_SHORTCONV_L_CACHE, "%s.shortconv.l_cache" },
// sentence-transformers dense modules feature dims
{ LLM_KV_DENSE_2_FEAT_IN, "%s.dense_2_feat_in" },
- { LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
- { LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
- { LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
+ { LLM_KV_DENSE_2_FEAT_OUT, "%s.dense_2_feat_out" },
+ { LLM_KV_DENSE_3_FEAT_IN, "%s.dense_3_feat_in" },
+ { LLM_KV_DENSE_3_FEAT_OUT, "%s.dense_3_feat_out" },
{ LLM_KV_TOKENIZER_MODEL, "tokenizer.ggml.model" },
{ LLM_KV_TOKENIZER_PRE, "tokenizer.ggml.pre" },
{ LLM_TENSOR_INDEXER_ATTN_Q_B, "blk.%d.indexer.attn_q_b" },
{ LLM_TENSOR_MASKED_EMBD_CENTROIDS, "masked_embd_centroids" },
{ LLM_TENSOR_MASKED_EMBD_ORDERING, "masked_embd_ordering" },
+ { LLM_TENSOR_FC, "fc" },
+ { LLM_TENSOR_D2T, "d2t" },
};
// declare information about the model weight tensors:
{LLM_TENSOR_FFN_LATENT_UP, {LLM_TENSOR_LAYER_REPEATING, GGML_OP_MUL_MAT}},
{LLM_TENSOR_MASKED_EMBD_CENTROIDS, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}},
{LLM_TENSOR_MASKED_EMBD_ORDERING, {LLM_TENSOR_LAYER_INPUT, GGML_OP_NONE}},
+ // eagle3
+ {LLM_TENSOR_FC, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_MUL_MAT}},
+ {LLM_TENSOR_D2T, {LLM_TENSOR_LAYER_OUTPUT, GGML_OP_GET_ROWS}},
};
LLM_KV::LLM_KV(llm_arch arch, const char * suffix) : arch(arch), suffix(suffix) {}
LLM_ARCH_KIMI_LINEAR,
LLM_ARCH_TALKIE,
LLM_ARCH_MELLUM,
+ LLM_ARCH_EAGLE3,
LLM_ARCH_UNKNOWN,
};
LLM_KV_CLASSIFIER_OUTPUT_LABELS,
+ LLM_KV_TARGET_LAYERS,
+ LLM_KV_TARGET_HIDDEN_SIZE,
+ LLM_KV_NORM_BEFORE_RESIDUAL,
+
LLM_KV_SHORTCONV_L_CACHE,
LLM_KV_XIELU_ALPHA_N,
LLM_TENSOR_NEXTN_SHARED_HEAD_NORM,
LLM_TENSOR_MASKED_EMBD_CENTROIDS,
LLM_TENSOR_MASKED_EMBD_ORDERING,
+ LLM_TENSOR_FC,
+ LLM_TENSOR_D2T,
};
cparams.no_perf = params.no_perf;
cparams.warmup = false;
+ cparams.embeddings_layer_inp.resize(hparams.n_layer(), false);
+ embd_layer_inp.resize(hparams.n_layer());
+
cparams.ctx_type = params.ctx_type;
cparams.pooling_type = params.pooling_type;
if (model.arch == LLM_ARCH_GEMMA4_ASSISTANT) {
if (params.ctx_other == nullptr) {
// TODO: change from runtime_error to llama_exception to avoid printing error message
- throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this is normal during memory fitting)");
+ throw std::runtime_error("Gemma4Assistant requires ctx_other to be set (this warning is normal during memory fitting)");
}
cparams.ctx_other = params.ctx_other;
}
+ if (model.arch == LLM_ARCH_EAGLE3) {
+ if (model.tok_embd == nullptr || model.output == nullptr) {
+ if (params.ctx_other == nullptr) {
+ throw std::runtime_error("EAGLE3 requires ctx_other to be set (this warning is normal during memory fitting)");
+ }
+ cparams.ctx_other = params.ctx_other;
+ }
+ }
+
// Initialize backend samplers here so they are part of the sampling graph
// before the reserve passes run later in this function. This avoids a later
// re-reserve when graph nodes change.
cparams.n_ubatch = std::min(cparams.n_batch, params.n_ubatch == 0 ? params.n_batch : params.n_ubatch);
- cparams.n_outputs_max = params.n_outputs_max == 0 ? cparams.n_batch : params.n_outputs_max;
+ cparams.n_outputs_max = params.n_outputs_max == 0 || llama_model_has_encoder(&model) ? cparams.n_batch : params.n_outputs_max;
cparams.op_offload = params.op_offload;
cparams.kv_unified = params.kv_unified;
}
}
+float * llama_context::get_embeddings_layer_inp(uint32_t lid) {
+ output_reorder();
+
+ GGML_ASSERT(lid < embd_layer_inp.size() && embd_layer_inp[lid].has_data());
+
+ return embd_layer_inp[lid].data;
+}
+
llama_token llama_context::get_sampled_token_ith(int32_t idx) {
output_reorder();
cparams.embeddings_nextn_masked = masked;
}
+void llama_context::set_embeddings_layer_inp(uint32_t lid, bool enable) {
+ LLAMA_LOG_DEBUG("%s: lid = %d, enable = %d\n", __func__, lid, enable);
+
+ GGML_ASSERT(lid < model.hparams.n_layer());
+
+ cparams.embeddings_layer_inp[lid] = enable;
+
+ // note: without this reserve, the draft acceptance drops to zero. not sure why - this is unexpected
+ sched_need_reserve = true;
+}
+
void llama_context::set_causal_attn(bool value) {
LLAMA_LOG_DEBUG("%s: value = %d\n", __func__, value);
const auto & hparams = model.hparams;
- const int64_t n_embd = hparams.n_embd_inp();
+ // eagle3/DFlash: features as encoder input, and non-draft paths fall back to model's input dim
+ const int64_t n_embd = hparams.n_embd_inp();
const int64_t n_vocab = model.vocab.n_tokens();
// note: during encode, we always pass the full sequence starting from pos = 0
}
}
+ extract_layer_inputs(res, n_tokens_prev, ubatch.n_tokens);
+
// extract nextn embeddings before
// only meaningful in LLAMA_POOLING_TYPE_NONE (per-token); other pooling modes are ignored.
{
const auto n_batch = cparams.n_batch;
const auto n_vocab = vocab.n_tokens();
+ const auto n_embd = hparams.n_embd;
const auto n_embd_out = hparams.n_embd_out();
bool has_logits = true;
has_embd = true;
}
-
size_t backend_float_count = 0;
size_t backend_token_count = 0;
+ size_t embd_layer_inp_float_count = 0;
logits.size = has_logits ? n_vocab*n_outputs_max : 0;
embd.size = has_embd ? n_embd_out*n_outputs_max : 0;
embd_nextn.size = (size_t) n_embd_out * n_batch;
}
+ for (bool enabled : cparams.embeddings_layer_inp) {
+ if (enabled) {
+ embd_layer_inp_float_count += (size_t) n_embd * n_batch;
+ }
+ }
+
// Allocate backend sampling output buffers if there are backend samplers configured.
const bool has_sampling = !sampling.samplers.empty();
if (has_sampling) {
const size_t prev_size = buf_output ? ggml_backend_buffer_get_size(buf_output.get()) : 0;
const size_t new_size =
- (logits.size + embd.size + embd_nextn.size + backend_float_count) * sizeof(float) +
- ( backend_token_count) * sizeof(llama_token);
+ (logits.size + embd.size + embd_nextn.size + embd_layer_inp_float_count + backend_float_count) * sizeof(float) +
+ ( backend_token_count) * sizeof(llama_token);
// alloc only when more than the current capacity is required
// TODO: also consider shrinking the buffer
logits.data = nullptr;
embd.data = nullptr;
embd_nextn.data = nullptr;
+ for (auto & layer_inp : embd_layer_inp) {
+ layer_inp = {nullptr, 0};
+ }
}
auto * buft = ggml_backend_cpu_buffer_type();
embd_nextn = has_embd_nextn ? buffer_view<float>{(float *) (base + offset), embd_nextn.size} : buffer_view<float>{nullptr, 0};
offset += embd_nextn.size * sizeof(float);
+ for (uint32_t il = 0; il < embd_layer_inp.size(); ++il) {
+ if (cparams.embeddings_layer_inp[il]) {
+ embd_layer_inp[il] = buffer_view<float>{(float *) (base + offset), (size_t) n_embd * n_batch};
+ offset += embd_layer_inp[il].size * sizeof(float);
+ } else {
+ embd_layer_inp[il] = buffer_view<float>{nullptr, 0};
+ }
+ }
+
if (has_sampling) {
sampling.logits = {(float *) (base + offset), (size_t)(n_vocab*n_outputs_max)};
offset += sampling.logits.size * sizeof(float);
return n_outputs_max;
}
+void llama_context::extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens) {
+ for (uint32_t il = 0; il < cparams.embeddings_layer_inp.size(); ++il) {
+ if (!cparams.embeddings_layer_inp[il]) {
+ continue;
+ }
+ if (!embd_layer_inp[il].has_data()) {
+ GGML_ABORT("output layer input buffer not allocated");
+ }
+ ggml_tensor * t = res->get_layer_inp((int) il);
+ if (!t) {
+ GGML_ABORT("layer input tensor not found");
+ }
+
+ const size_t nbytes = ggml_nbytes(t);
+ const size_t nfloats = nbytes / sizeof(float);
+ GGML_ASSERT(n_tokens > 0);
+ GGML_ASSERT(nfloats % n_tokens == 0);
+
+ const size_t row_floats = nfloats / n_tokens;
+ const size_t dst_offset = token_offset * row_floats;
+ GGML_ASSERT(dst_offset + nfloats <= embd_layer_inp[il].size);
+
+ ggml_backend_t backend = ggml_backend_sched_get_tensor_backend(sched.get(), t);
+ GGML_ASSERT(backend != nullptr);
+ ggml_backend_tensor_get_async(backend, t, embd_layer_inp[il].data + dst_offset, 0, nbytes);
+ }
+}
+
void llama_context::output_reorder() {
const uint64_t n_vocab = model.vocab.n_tokens();
const uint64_t n_embd = model.hparams.n_embd;
}
}
+ if (embd_layer_inp.size() > 0) {
+ for (int lid = 0; lid < (int) embd_layer_inp.size(); ++lid) {
+ if (embd_layer_inp[lid].size > 0) {
+ for (uint64_t k = 0; k < n_embd; ++k) {
+ std::swap(embd_layer_inp[lid].data[i0*n_embd + k], embd_layer_inp[lid].data[i1*n_embd + k]);
+ }
+ }
+ }
+ }
+
if (!sampling.samplers.empty()) {
assert(sampling.logits.size > 0);
assert(sampling.probs.size > 0);
ctx->set_embeddings_nextn(value, masked);
}
+void llama_set_embeddings_layer_inp(llama_context * ctx, uint32_t lid, bool value) {
+ ctx->set_embeddings_layer_inp(lid, value);
+}
+
llama_memory_t llama_get_memory(const struct llama_context * ctx) {
if (!ctx) {
return nullptr;
return ctx->get_embeddings_nextn_ith(i);
}
+float * llama_get_embeddings_layer_inp(llama_context * ctx, uint32_t lid) {
+ ctx->synchronize();
+
+ return ctx->get_embeddings_layer_inp(lid);
+}
+
bool llama_set_sampler(llama_context * ctx, llama_seq_id seq_id, llama_sampler * smpl) {
return ctx->set_sampler(seq_id, smpl);
}
float * get_embeddings_nextn();
float * get_embeddings_nextn_ith(int32_t i);
+ float * get_embeddings_layer_inp(uint32_t lid);
+
llama_token * get_sampled_tokens() const;
llama_token get_sampled_token_ith(int32_t idx);
void set_embeddings (bool value);
void set_embeddings_nextn(bool value, bool masked);
+ void set_embeddings_layer_inp(uint32_t lid, bool enable);
void set_causal_attn(bool value);
void set_warmup(bool value);
// map the output row index `i` to batch index
int64_t output_resolve_row(int32_t i) const;
+ // async-copy enabled layer-input tensors (per cparams.output_layer_inp)
+ // from backend into host-side embd_layer_inp buffers
+ void extract_layer_inputs(const llm_graph_result * res, size_t token_offset, size_t n_tokens);
+
//
// graph
//
// sets llm_graph_result::t_h_nextn
buffer_view<float> embd_nextn = {nullptr, 0};
+ // host buffers for output layer input embeddings, per layer
+ // populated when cparams.output_layer_inp[il] is true
+ std::vector<buffer_view<float>> embd_layer_inp;
+
struct sampling_info {
// !samplers.empty() to check if any samplers are active
std::map<llama_seq_id, llama_sampler *> samplers;
#include "llama.h"
#include <cstdint>
+#include <vector>
#define LLAMA_MAX_SEQ 256
bool kv_unified;
bool pipeline_parallel;
+ std::vector<bool> embeddings_layer_inp; // [n_layer()] extract input embeddings for layer
+
enum llama_context_type ctx_type;
enum llama_pooling_type pooling_type;
// LLAMA_API float * llama_get_embeddings_ith(struct llama_context * ctx, int32_t i);
LLAMA_API float * llama_get_embeddings_nextn_ith(struct llama_context * ctx, int32_t i);
+// Set whether the context outputs the input embeddings of a specific layer
+LLAMA_API void llama_set_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid, bool value);
+
+// mirrors:
+// LLAMA_API float * llama_get_embeddings(struct llama_context * ctx);
+LLAMA_API float * llama_get_embeddings_layer_inp(struct llama_context * ctx, uint32_t lid);
+
LLAMA_API llama_context * llama_get_ctx_other(struct llama_context * ctx);
+
+//
+// model/context data extraction
+//
+
+// returns pointer to the target-model layer indices
+LLAMA_API const int32_t * llama_model_target_layer_ids (const struct llama_model * model);
+// returns the number of extracted layers from target model
+LLAMA_API uint32_t llama_model_target_layer_ids_n(const struct llama_model * model);
t_logits = nullptr;
t_embd = nullptr;
t_embd_pooled = nullptr;
+
+ t_layer_inp.resize(LLAMA_MAX_LAYERS);
+ std::fill(t_layer_inp.begin(), t_layer_inp.end(), nullptr);
+
t_sampled.clear();
t_sampled_probs.clear();
t_sampled_logits.clear();
}
}
-void llm_graph_result::set_outputs() {
+void llm_graph_result::set_outputs(const llm_graph_params & params) {
if (t_logits != nullptr) {
ggml_set_output(t_logits);
}
if (t_h_nextn != nullptr) {
ggml_set_output(t_h_nextn);
}
+ {
+ const auto & embeddings_layer_inp = params.cparams.embeddings_layer_inp;
+ for (size_t il = 0; il < embeddings_layer_inp.size(); ++il) {
+ if (embeddings_layer_inp[il]) {
+ GGML_ASSERT(t_layer_inp[il] != nullptr && "layer input tensor is null");
+ ggml_set_output(t_layer_inp[il]);
+ }
+ }
+ }
for (auto & [seq_id, t] : t_sampled) {
if (t != nullptr) {
ggml_set_output(t);
ggml_tensor * get_embd_pooled() const { return t_embd_pooled; }
ggml_tensor * get_h_nextn() const { return t_h_nextn; }
+ ggml_tensor * get_layer_inp(int il) const { return t_layer_inp[il]; }
+
ggml_cgraph * get_gf() const { return gf; }
ggml_context * get_ctx() const { return ctx_compute.get(); }
void reset();
void set_inputs(const llama_ubatch * ubatch);
- void set_outputs();
+ void set_outputs(const llm_graph_params & params);
// try to update the existing graph result using the new graph parameters in order to reuse it
// this can only be done if we determine that the resulting graph using the new graph parameters
ggml_tensor * t_embd_pooled = nullptr;
ggml_tensor * t_h_nextn = nullptr; // [n_embd, n_outputs] hidden state before final output norm
- std::map<llama_seq_id, ggml_tensor*> t_sampled_logits;
- std::map<llama_seq_id, ggml_tensor*> t_candidates;
- std::map<llama_seq_id, ggml_tensor*> t_sampled;
- std::map<llama_seq_id, ggml_tensor*> t_sampled_probs;
+ std::vector<ggml_tensor *> t_layer_inp;
+
+ std::map<llama_seq_id, ggml_tensor *> t_sampled_logits;
+ std::map<llama_seq_id, ggml_tensor *> t_candidates;
+ std::map<llama_seq_id, ggml_tensor *> t_sampled;
+ std::map<llama_seq_id, ggml_tensor *> t_sampled_probs;
std::vector<llm_graph_input_ptr> inputs;
bool rope_finetuned;
bool use_par_res;
bool swin_norm;
+ bool norm_before_residual = false;
uint32_t n_ctx_train; // context size the model was trained on
uint32_t n_embd;
template bool llama_model_loader::get_arr<std::vector<std::string>>(enum llm_kv kid, std::vector<std::string> & result, bool required);
template bool llama_model_loader::get_arr<std::array<int32_t, 512>>(enum llm_kv kid, std::array<int32_t, 512> & result, bool required);
+ template bool llama_model_loader::get_arr<std::vector<int32_t>>(enum llm_kv kid, std::vector<int32_t> & result, bool required);
template<typename T>
bool llama_model_loader::get_key(const std::string & key, T & result, bool required) {
return new llama_model_qwen35moe(params);
case LLM_ARCH_MISTRAL3:
return new llama_model_mistral3(params);
+ case LLM_ARCH_EAGLE3:
+ return new llama_model_eagle3(params);
case LLM_ARCH_MIMO2:
return new llama_model_mimo2(params);
case LLM_ARCH_KIMI_LINEAR:
// TODO: move reranking logic here and generalize
llm->build_dense_out(dense_2_out_layers, dense_2_out_layers_b, dense_3_out_layers);
- llm->res->set_outputs();
+ llm->res->set_outputs(params);
return llm->res->get_gf();
}
case LLM_ARCH_ERNIE4_5:
case LLM_ARCH_ERNIE4_5_MOE:
case LLM_ARCH_MISTRAL3:
+ case LLM_ARCH_EAGLE3:
case LLM_ARCH_MISTRAL4:
case LLM_ARCH_LLAMA_EMBED:
case LLM_ARCH_MAINCODER:
bool llama_model_has_encoder(const llama_model * model) {
switch (model->arch) {
- case LLM_ARCH_T5: return true;
- case LLM_ARCH_T5ENCODER: return true;
+ case LLM_ARCH_T5:
+ case LLM_ARCH_T5ENCODER:
+ case LLM_ARCH_EAGLE3: return true;
default: return false;
}
}
layer.wv_b = create_tensor(tn(LLM_TENSOR_ATTN_V, "bias", bid), {n_embd_v_}, TENSOR_NOT_REQUIRED);
}
}
+
+const int32_t * llama_model_target_layer_ids(const struct llama_model * model) {
+ const auto & v = model->target_layer_ids;
+ return v.empty() ? nullptr : v.data();
+}
+
+uint32_t llama_model_target_layer_ids_n(const struct llama_model * model) {
+ return (uint32_t) model->target_layer_ids.size();
+}
struct ggml_tensor * per_layer_model_proj = nullptr;
struct ggml_tensor * per_layer_proj_norm = nullptr;
+ // eagle3
+ struct ggml_tensor * fc = nullptr; // feature fusion layer
+ struct ggml_tensor * d2t = nullptr; // draft to target vocabulary mapping
+
+ // unified vector to store target-model extracted layer ids in eagle3, dflash, etc.
+ std::vector<int32_t> target_layer_ids;
+
std::vector<llama_layer> layers;
//Dense linear projections for SentenceTransformers models like embeddinggemma
--- /dev/null
+#include "models.h"
+
+void llama_model_eagle3::load_arch_hparams(llama_model_loader & ml) {
+ ml.get_key(LLM_KV_ATTENTION_LAYERNORM_RMS_EPS, hparams.f_norm_rms_eps);
+
+ if (!ml.get_arr(LLM_KV_TARGET_LAYERS, target_layer_ids, false)) {
+ throw std::runtime_error("EAGLE3 model requires 'extract_layers' in GGUF metadata");
+ }
+ if (target_layer_ids.size() != 3) {
+ throw std::runtime_error("EAGLE3 requires exactly 3 entries in 'extract_layers'");
+ }
+ LLAMA_LOG_INFO("%s: EAGLE3 extract_layers = [%d, %d, %d]\n", __func__,
+ target_layer_ids[0],
+ target_layer_ids[1],
+ target_layer_ids[2]);
+
+ uint32_t n_embd_tgt = 0;
+
+ ml.get_key(LLM_KV_TARGET_HIDDEN_SIZE, n_embd_tgt);
+ LLAMA_LOG_INFO("%s: EAGLE3 n_embd_tgt = %u (draft n_embd = %u)\n", __func__, n_embd_tgt, hparams.n_embd);
+
+ hparams.n_embd_inp_impl = (uint32_t) target_layer_ids.size() * n_embd_tgt;
+
+ // eagle3 norm_before_residual (optional, default false)
+ // compatible with Readhat eagle3 speculator model
+ ml.get_key(LLM_KV_NORM_BEFORE_RESIDUAL, hparams.norm_before_residual, false);
+ if (hparams.norm_before_residual) {
+ LLAMA_LOG_INFO("%s: EAGLE3gnorm_before_residual = true\n", __func__);
+ }
+
+ type = LLM_TYPE_UNKNOWN;
+}
+
+void llama_model_eagle3::load_arch_tensors(llama_model_loader &) {
+ LLAMA_LOAD_LOCALS;
+
+ const int64_t n_embd_inp = hparams.n_embd_inp();
+ const int64_t n_embd_attn_input = 2 * n_embd;
+
+ // Get vocab size from the d2t tensor in the GGUF file (optional - only needed if eagle3 has different vocab_size than target)
+ // d2t: draft to target vocabulary mapping
+ int64_t n_draft_vocab = n_vocab; // Default: same as target vocab
+ const struct ggml_tensor * d2t_meta = ml->get_tensor_meta("d2t");
+ if (d2t_meta) {
+ n_draft_vocab = d2t_meta->ne[0]; // update draft vocab size
+ d2t = create_tensor(tn(LLM_TENSOR_D2T), {n_draft_vocab}, 0);
+ LLAMA_LOG_INFO("%s: EAGLE3 using d2t mapping (draft_vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+ } else {
+ d2t = nullptr; // no d2t, use default vocab size
+ LLAMA_LOG_INFO("%s: EAGLE3 without d2t - sharing same vocab_size with target (vocab_size = %lld)\n", __func__, (long long)n_draft_vocab);
+ }
+
+ // Feature fusion layer: projects 3 target layers to draft hidden size
+ fc = create_tensor(tn(LLM_TENSOR_FC, "weight"), {n_embd_inp, n_embd}, 0);
+
+ // Output layer (uses draft vocab size)
+ output_norm = create_tensor(tn(LLM_TENSOR_OUTPUT_NORM, "weight"), {n_embd}, 0);
+ output = create_tensor(tn(LLM_TENSOR_OUTPUT, "weight"), {n_embd, n_draft_vocab}, TENSOR_NOT_REQUIRED);
+
+ // Token embeddings (optional - Llama 3.3 70B EAGLE3 has its own)
+ const struct ggml_tensor * tok_embd_meta = ml->get_tensor_meta(tn(LLM_TENSOR_TOKEN_EMBD, "weight").str().c_str());
+ if (tok_embd_meta) {
+ const int64_t n_target_vocab = tok_embd_meta->ne[1];
+ tok_embd = create_tensor(tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_target_vocab}, 0);
+ LLAMA_LOG_INFO("%s: EAGLE3 using its own token_embd (vocab = %lld)\n", __func__, (long long)n_target_vocab);
+ }
+
+ // Single decoder layer
+ for (int i = 0; i < n_layer; ++i) {
+ auto & layer = layers[i];
+
+ // input_layernorm: applied to token embeddings
+ layer.attn_norm = create_tensor(tn(LLM_TENSOR_ATTN_NORM, "weight", i), {n_embd}, 0);
+
+ // eagle3 specific: hidden_norm applied to fused target features
+ layer.attn_norm_2 = create_tensor(tn(LLM_TENSOR_ATTN_NORM_2, "weight", i), {n_embd}, 0);
+
+ // Attention takes input_embeds_normed + fused_target_normed as input
+ layer.wq = create_tensor(tn(LLM_TENSOR_ATTN_Q, "weight", i), {n_embd_attn_input, n_embd_head_k * n_head}, 0);
+ layer.wk = create_tensor(tn(LLM_TENSOR_ATTN_K, "weight", i), {n_embd_attn_input, n_embd_k_gqa}, 0);
+ layer.wv = create_tensor(tn(LLM_TENSOR_ATTN_V, "weight", i), {n_embd_attn_input, n_embd_v_gqa}, 0);
+ layer.wo = create_tensor(tn(LLM_TENSOR_ATTN_OUT, "weight", i), {n_embd_head_k * n_head, n_embd}, 0);
+
+ layer.ffn_norm = create_tensor(tn(LLM_TENSOR_FFN_NORM, "weight", i), {n_embd}, 0);
+ layer.ffn_gate = create_tensor(tn(LLM_TENSOR_FFN_GATE, "weight", i), {n_embd, n_ff}, 0);
+ layer.ffn_down = create_tensor(tn(LLM_TENSOR_FFN_DOWN, "weight", i), { n_ff, n_embd}, 0);
+ layer.ffn_up = create_tensor(tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff}, 0);
+
+ // rope_freqs for llama3 rope scaling (optional - only if eagle3 config has rope_scaling)
+ layer.rope_freqs = create_tensor(tn(LLM_TENSOR_ROPE_FREQS, "weight", i), {n_rot/2}, TENSOR_NOT_REQUIRED);
+ }
+}
+
+std::unique_ptr<llm_graph_context> llama_model_eagle3::build_arch_graph(const llm_graph_params & params) const {
+ switch (params.gtype) {
+ case LLM_GRAPH_TYPE_ENCODER:
+ return std::make_unique<graph<true>>(*this, params);
+ case LLM_GRAPH_TYPE_DEFAULT:
+ case LLM_GRAPH_TYPE_DECODER:
+ return std::make_unique<graph<false>>(*this, params);
+ default:
+ GGML_ABORT("invalid graph type");
+ };
+}
+
+template <>
+ggml_tensor * llama_model_eagle3::graph<true>::build_inp_embd_enc() const {
+ ggml_tensor * cur = nullptr;
+
+ // Input: Target model features (3 layers concatenated: low, mid, high)
+ // Data will be provided via ubatch->embd in encode_eagle3_features()
+ auto inp_target = std::make_unique<llm_graph_input_embd>(hparams.n_embd_inp());
+ inp_target->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32,hparams.n_embd_inp(), n_tokens);
+ ggml_set_input(inp_target->embd);
+
+ cur = inp_target->embd;
+ cb(cur, "inp_embd", -1);
+
+ res->add_input(std::move(inp_target));
+
+ return cur;
+}
+
+// eagle3 Encoder: processes target model features through feature fusion layer
+// Input: target_features e.g. [12288, n_tokens] from target model layers low, middle, high
+// Output: g_embeddings e.g. [4096, n_tokens] stored in context
+template <>
+llama_model_eagle3::graph<true>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ ggml_tensor * cur = nullptr;
+
+ cur = build_inp_embd_enc();
+
+ // Feature fusion layer
+ cur = build_lora_mm(model.fc, cur);
+ cb(cur, "fc_out", -1);
+
+ // Output: g_embeddings e.g. [4096, n_tokens]
+ // store in t_h_nextn (same as MTP) so can be read via llama_get_embeddings_nextn(ctx_dft)
+ ggml_set_output(cur);
+ res->t_h_nextn = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
+
+// eagle3 Decoder: processes draft tokens using g_embeddings from encoder
+// Input: draft tokens + g_embeddings from encoder
+// Output: draft logits
+template <>
+llama_model_eagle3::graph<false>::graph(const llama_model & model, const llm_graph_params & params) : llm_graph_context(params) {
+ const int64_t n_embd_head = hparams.n_embd_head_v();
+
+ GGML_ASSERT(n_embd_head == hparams.n_embd_head_k());
+ GGML_ASSERT(n_layer == 1); // eagle3 has only one decoder layer
+
+ ggml_tensor * cur;
+ ggml_tensor * inpL;
+
+ // eagle3 Decoder receives:
+ // 1. Token embeddings (e.g.from eagle3's own tok_embd for Llama 3.3 70B, or target model for Llama 3.1 8B)
+ // 2. g_embeddings from encoder
+ auto * tok_embd = model.tok_embd;
+ if (model.tok_embd == nullptr) {
+ GGML_ASSERT(cparams.ctx_other != nullptr);
+ const auto * model_other = llama_get_model(cparams.ctx_other);
+
+ GGML_ASSERT(model_other->tok_embd != nullptr && "EAGLE3 decoder requires token embeddings (own or from target model)");
+ tok_embd = model_other->tok_embd;
+ }
+
+ auto inp = std::make_unique<llm_graph_input_embd>(n_embd);
+
+ inp->tokens = ggml_new_tensor_1d(ctx0, GGML_TYPE_I32, n_tokens);
+ ggml_set_input(inp->tokens);
+
+ inp->embd = ggml_new_tensor_2d(ctx0, GGML_TYPE_F32, n_embd, n_tokens);
+ ggml_set_input(inp->embd);
+
+ ggml_tensor * inp_embd = ggml_get_rows(ctx0, tok_embd, inp->tokens);
+ cb(inp_embd, "inp_embd", -1);
+
+ ggml_tensor * inp_g = inp->embd;
+ cb(inp_g, "inp_g_embeddings", -1);
+
+ res->add_input(std::move(inp));
+
+ inpL = inp_g;
+
+ // inp_pos - contains the positions
+ ggml_tensor * inp_pos = build_inp_pos();
+
+ auto * inp_attn = build_attn_inp_kv();
+
+ const float kq_scale = 1.0f/sqrtf(float(n_embd_head));
+
+ // Single decoder layer (il = 0)
+ const int il = 0;
+ {
+ // Apply input_layernorm to the token embeddings
+ ggml_tensor * embd_norm = build_norm(inp_embd,
+ model.layers[il].attn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(embd_norm, "embd_norm", il);
+
+ // Apply hidden_norm to inp_g
+ ggml_tensor * g_norm = build_norm(inp_g,
+ model.layers[il].attn_norm_2, NULL,
+ LLM_NORM_RMS, -1);
+ cb(g_norm, "g_norm", il);
+
+ // norm_before_residual: determines what goes into the residual connection (compatible with Readhat eagle3 speculator model)
+ // - false (default): use raw inp_g for residual
+ // - true: use normalized g_norm for residual
+ // inpL is the concatenated input (normalized inp_embd + normalized inp_g)
+ ggml_tensor * inpSA = hparams.norm_before_residual ? g_norm : inpL;
+
+ // Concatenate normalized inp_embd and normalized inp_g
+ cur = ggml_concat(ctx0, embd_norm, g_norm, il);
+ cb(cur, "concat_embd", il);
+
+ // Self-attention with concatenated input
+ ggml_tensor * Qcur = build_lora_mm(model.layers[il].wq, cur);
+ cb(Qcur, "Qcur", il);
+
+ ggml_tensor * Kcur = build_lora_mm(model.layers[il].wk, cur);
+ cb(Kcur, "Kcur", il);
+
+ ggml_tensor * Vcur = build_lora_mm(model.layers[il].wv, cur);
+ cb(Vcur, "Vcur", il);
+
+ Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
+ Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
+ Vcur = ggml_reshape_3d(ctx0, Vcur, n_embd_head, n_head_kv, n_tokens);
+
+ // rope freq factors, returns nullptr if not available
+ ggml_tensor * rope_factors = model.get_rope_factors(cparams, il);
+
+ // RoPE
+ Qcur = ggml_rope_ext(
+ ctx0, Qcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+ Kcur = ggml_rope_ext(
+ ctx0, Kcur, inp_pos, rope_factors,
+ n_rot, rope_type, n_ctx_orig, freq_base, freq_scale,
+ ext_factor, attn_factor, beta_fast, beta_slow
+ );
+
+ cb(Qcur, "Qcur_rope", il);
+ cb(Kcur, "Kcur_rope", il);
+
+ cur = build_attn(inp_attn,
+ model.layers[il].wo, NULL, nullptr,
+ Qcur, Kcur, Vcur, nullptr, nullptr, nullptr, kq_scale, il);
+
+ // Add residual and update it
+ ggml_tensor * ffn_inp = ggml_add(ctx0, cur, inpSA);
+ cb(ffn_inp, "ffn_inp", il);
+
+ // Apply FFN norm to the sum
+ cur = build_norm(ffn_inp,
+ model.layers[il].ffn_norm, NULL,
+ LLM_NORM_RMS, il);
+ cb(cur, "post_attn_norm", il);
+
+ cur = build_ffn(cur,
+ model.layers[il].ffn_up, NULL, NULL,
+ model.layers[il].ffn_gate, NULL, NULL,
+ model.layers[il].ffn_down, NULL, NULL,
+ NULL,
+ LLM_FFN_SILU, LLM_FFN_PAR, il);
+ cb(cur, "ffn_out", il);
+
+ // Output norm with residual
+ cur = ggml_add(ctx0, cur, ffn_inp);
+ cb(cur, "eagle3_prenorm", il);
+
+ inpL = cur;
+ }
+
+ cur = inpL;
+
+ // Output prenorm state (for next token's g_embeddings in autoregressive generation)
+ ggml_set_output(cur);
+ res->t_h_nextn = cur;
+
+ cur = build_norm(cur,
+ model.output_norm, NULL,
+ LLM_NORM_RMS, -1);
+ cb(cur, "result_norm", -1);
+
+ // lm_head - projects to draft vocabulary
+ // if the draft has no own output projection, inherit the target model's lm_head
+ auto * output = model.output;
+ if (output == nullptr) {
+ GGML_ASSERT(cparams.ctx_other != nullptr);
+ const auto * model_other = llama_get_model(cparams.ctx_other);
+
+ GGML_ASSERT(model_other->output != nullptr && "EAGLE3 decoder requires an output projection (own or from target model)");
+ output = model_other->output;
+ }
+ cur = build_lora_mm(output, cur);
+
+ if (model.d2t) {
+ const int64_t n_draft_vocab = cur->ne[0];
+ const int64_t n_outputs = cur->ne[1];
+ const int64_t n_vocab = (int64_t) model.vocab.n_tokens();
+
+ GGML_ASSERT(model.d2t->type == GGML_TYPE_I64);
+ GGML_ASSERT(model.d2t->ne[0] == n_draft_vocab);
+
+ ggml_tensor * logits = ggml_fill(ctx0, ggml_new_tensor_3d(ctx0, GGML_TYPE_F32, 1, n_vocab, n_outputs), -INFINITY);
+ cur = ggml_set_rows(ctx0, logits,
+ ggml_reshape_3d(ctx0, cur, 1, n_draft_vocab, n_outputs),
+ ggml_reshape_3d(ctx0, model.d2t, n_draft_vocab, 1, 1));
+ cur = ggml_reshape_2d(ctx0, cur, n_vocab, n_outputs);
+ }
+
+ cb(cur, "result_output", -1);
+ res->t_logits = cur;
+
+ ggml_build_forward_expand(gf, cur);
+}
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
const int n_rot_l = hparams.n_rot(il);
+ res->t_layer_inp[il] = inpL;
+
// norm
cur = build_norm(inpL, model.layers[il].attn_norm, nullptr, LLM_NORM_RMS, il);
cb(cur, "attn_norm", il);
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
};
+struct llama_model_eagle3 : public llama_model_base {
+ llama_model_eagle3(const struct llama_model_params & params) : llama_model_base(params) {}
+ void load_arch_hparams(llama_model_loader & ml) override;
+ void load_arch_tensors(llama_model_loader & ml) override;
+
+ template <bool is_enc>
+ struct graph : public llm_graph_context {
+ graph(const llama_model & model, const llm_graph_params & params);
+
+ ggml_tensor * build_inp_embd_enc() const;
+ };
+
+ std::unique_ptr<llm_graph_context> build_arch_graph(const llm_graph_params & params) const override;
+};
+
struct llama_model_mistral4 : public llama_model_deepseek2 {
llama_model_mistral4(const struct llama_model_params & params) : llama_model_deepseek2(params) {}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
const float freq_base_l = model.get_rope_freq_base (cparams, il);
const float freq_scale_l = model.get_rope_freq_scale(cparams, il);
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
}
if (il == n_layer - 1 && inp_out_ids && cparams.embeddings_nextn_masked) {
- cur = ggml_get_rows(ctx0, cur, inp_out_ids);
+ cur = ggml_get_rows(ctx0, cur, inp_out_ids);
inpSA = ggml_get_rows(ctx0, inpSA, inp_out_ids);
}
ggml_tensor * inp_out_ids = build_inp_out_ids();
for (int il = 0; il < n_layer; ++il) {
+ res->t_layer_inp[il] = inpL;
+
ggml_tensor * inpSA = inpL;
// norm
if (arch == LLM_ARCH_GEMMA4 || arch == LLM_ARCH_GEMMA4_ASSISTANT) {
continue; // FIXME: ISWA KV cache initialization needs more fixture params
}
+ if (arch == LLM_ARCH_EAGLE3) {
+ continue;
+ }
for (bool moe : {false, true}) {
if (moe && !moe_implemented(arch)) {
continue;
if (arch == LLM_ARCH_GEMMA4 || arch == LLM_ARCH_GEMMA4_ASSISTANT) {
continue; // FIXME: ISWA KV cache initialization needs more fixture params
}
+ if (arch == LLM_ARCH_EAGLE3) {
+ continue;
+ }
const bool encode = arch == LLM_ARCH_T5 || arch == LLM_ARCH_DREAM || arch == LLM_ARCH_LLADA || arch == LLM_ARCH_LLADA_MOE || arch == LLM_ARCH_RND1;
for (bool moe : {false, true}) {
overview / pkg / ggml / sources / llama.cpp / commitdiff